Introduction

There has been tremendous growth in the area of undergraduate biology research over the last fifteen years. This study attempts to summarize and analyze the progress of this growth for the journal of CBE-LSE.

Summary of initial data set

## 
## Converting your wos collection into a bibliographic dataframe
## 
## 
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
## 
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html)
## 
## 
## Missing fields:  DE 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

After the results are processed, they are stored as data tables for information reporting.

##                                Description   Results
## 1              MAIN INFORMATION ABOUT DATA          
## 2                                 Timespan 2008:2022
## 3           Sources (Journals, Books, etc)         1
## 4                                Documents      1072
## 5  Annual Growth Rate %                  -      1.83
## 6                     Document Average Age      7.54
## 7                Average citations per doc      20.3
## 8       Average citations per year per doc      2.23
## 9                               References     26262
## 10                          DOCUMENT TYPES          
## 11                                 article       920
## 12                            bibliography         1
## 13                             book review         1
## 14                              correction        22
## 15                      editorial material        90
## 16                                  letter        33
## 17                                  review         5
## 18                       DOCUMENT CONTENTS          
## 19                      Keywords Plus (ID)      1302
## 20                  Author's Keywords (DE)         0
## 21                                 AUTHORS          
## 22                                 Authors      3187
## 23                      Author Appearances      4746
## 24         Authors of single-authored docs        93
## 25                   AUTHORS COLLABORATION          
## 26                    Single-authored docs       157
## 27                    Documents per Author     0.336
## 28                      Co-Authors per Doc      4.43
## 29          International co-authorships %     4.571
## 30

Research Questions

Basic overview of the field

A timeline of the publication productivity is included for the journal.

Authoring

We focused on the most productive authors within the data set by H-index.

##    Authors        Articles Authors        Articles Fractionalized
## 1     BROWNELL SE       36    TANNER KD                     14.79
## 2     TANNER KD         34    DOLAN EL                      12.34
## 3     DOLAN EL          28    ALLEN D                       12.31
## 4     KNIGHT JK         26    EDDY SL                       11.17
## 5     EDDY SL           21    STARK LA                      10.29
## 6     SMITH MK          21    DOLAN E                        9.00
## 7     COOPER KM         19    BROWNELL SE                    8.57
## 8     ALLEN D           15    HOOPES LLM                     7.03
## 9     HOOPES LLM        15    KNIGHT JK                      6.94
## 10    STARK LA          15    GOUVEA JS                      5.33

We display a timeline of the productive authors.

##               
##                2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
##   BROWNELL SE     0    0    0    0    1    0    3    2    3    7    3    2    7    5    3
##   COOPER KM       0    0    0    0    0    0    0    0    1    3    1    1    4    6    3
##   COUCH BA        0    0    0    0    0    0    0    2    3    3    1    2    1    2    0
##   DOLAN EL        0    0    2    0    2    2    3    3    4    4    2    2    2    1    1
##   EDDY SL         0    0    0    0    0    0    2    3    2    4    2    2    3    2    1
##   FREEMAN S       0    0    1    1    0    0    2    0    0    1    1    2    0    0    0
##   GRAHAM MJ       0    0    0    0    0    0    0    3    3    0    2    1    1    1    2
##   HANAUER DI      0    0    0    0    1    0    2    2    2    0    2    0    0    1    2
##   KNIGHT JK       1    0    2    2    0    2    0    3    3    2    3    4    3    1    0
##   PRICE RM        0    0    0    0    1    2    3    0    0    2    1    0    0    1    1
##   SMITH MK        1    0    1    3    0    2    2    1    2    1    2    3    2    1    0
##   TANNER KD       1    2    1    3    4    3    1    3    0    4    2    2    4    3    1
##   WENDEROTH MP    1    0    1    0    0    1    2    2    1    1    1    1    1    0    0

Institutions

Affiliations around the world

{r world corresponding author affiliations, echo=FALSE} # world_map = map_data("world") %>% # filter(long < 180) # # countries <- world_map %>% # distinct(region) %>% # rowid_to_column() # # allcountries <- data.frame(countries = countries$region, count = NA) # # for (country in countries$region) { # allcountries[countries == country] <- sum(str_count(data$C1,str_to_upper(country)),na.rm = TRUE) # } # # countryfreq <- results$Countries %>% # as.data.frame() %>% # mutate(Tab = str_to_title(Tab)) %>% # mutate(Tab = case_when( # Tab == "Usa" ~ "USA", # Tab == "United Kingdom" ~"UK", # TRUE ~ Tab # )) # # # # countryfreq <- allcountries %>% # filter(count > 0) %>% # mutate(region = countries) %>% # full_join(countryfreq,by = c("region" = "Tab")) %>% # mutate(coor_author_freq = Freq, Freq = count, Tab = region) # # world_map_short <- world_map %>% # full_join(countryfreq, by = c("region" = "Tab")) %>% # filter(Freq >= 1 & (subregion != "Alaska" | region != "New Zealand"))#%>% # #mutate(case_when(region == "USA", )) # # worldcentroids <- world_map_short %>% # group_by(region, Freq) %>% # filter(subregion != "Alaska" & subregion != "Pitt Island" & subregion != "Chatham Island" & subregion != "12"& subregion != "13") %>% # summarise(latitude = sign(lat) * exp(mean(log(abs(lat)))), longitude = sign(long) * exp(mean(log(abs(long))))) # # # AffiliationWorldMap <- countries %>% # full_join(countryfreq, by = c("region" = "region")) %>% # ggplot(aes(fill = Freq, map_id = region), color = 1) + # #geom_hline(yintercept=0, color = "light grey") + # geom_map(map = world_map_short, colour = "white", lwd = 0.2) + # geom_map(map = world_map, colour = "light grey", lwd = 0.2)+ # geom_point(data = worldcentroids, aes(x = longitude, y = latitude, size = Freq*100, group = region),color = "#333333") + # geom_text(data = worldcentroids, aes(x = longitude, y = latitude, label = Freq, size = Freq, group = region), color = "white") + # expand_limits(x = world_map_short$long, y = world_map_short$lat) + # coord_map("moll") + # #coord_quickmap()+ # #theme_map()+ # theme_default + # #labs(caption = "") + # theme(#plot.margin= unit(c(0.5, 0.5, 0.5, 0.5), "cm"), # # plot.caption = element_text(hjust = 0, face= "italic"), # axis.title = element_text(size = 14, color = "#555555"), # plot.title.position = "plot", # plot.caption.position = "plot", # legend.title = element_blank(), # #panel.background = element_rect(fill = "aliceblue", color = "aliceblue"), # legend.position = c(0.5, 0.01), # legend.background = element_rect(fill = NA), # legend.direction = "horizontal", # #legend.key.size = unit(0.4, "cm"), # legend.key.height= unit(.5, 'cm'), # legend.key.width= unit(.5, 'cm'), # panel.border = element_blank(), # panel.grid = element_blank(), # plot.background = element_blank() # #plot.caption.position = "plot", # #plot.caption = element_text(hjust = 0) # #legend.margin = margin(6, 12, 4, 1) # #legend.position = "none" # ) + # scale_fill_carto_c(palette = "BluGrn", direction = 1, # # trans = scales::pseudo_log_trans(sigma = 0.01), # # breaks = c(1,10, 100, 1000), # # labels = c(1,10, 100, 1000), # # limits = c(1,1000) # ) + # scale_size(range = c(3,14))+ # labs(fill = "Count of article", # title = "A. Published by country", # # x = NULL, # y = NULL # ) # # AffiliationWorldMap # # ggsave("figures/AffiliationWorldMapshort.jpg", device = "jpeg", dpi = 400, width = 7, # height = 3, # units = "in", limitsize = FALSE) #

Affiliations in the US

## # A tibble: 229 × 7
##     ...1 AFF...2                          AFF...3                           Freq OPEID Issues                      ...7 
##    <dbl> <chr>                            <chr>                            <dbl> <chr> <chr>                       <chr>
##  1   478 DIV EARTH AND LIFE SCI           DIV EARTH AND LIFE SCI               1 na    DC                          <NA> 
##  2   501 FRANCE.                          FRANCE.                              1 na    ??                          <NA> 
##  3   531 INDIAN INST MANAGEMENT AHMEDABAD INDIAN INST MANAGEMENT AHMEDABAD     1 na    Indian Institute of Manage… India
##  4   563 LIBERAL ARTS AND SCI ACAD        LIBERAL ARTS AND SCI ACAD            1 na    public magnet high school … <NA> 
##  5   571 MARCO POLO TECHNOL               MARCO POLO TECHNOL                   1 na    ??                          <NA> 
##  6   591 MT DESERT ISL BIOL LAB           MT DESERT ISL BIOL LAB               1 na    Mount Desert Island Biolog… Maine
##  7   594 NA                               NA                                   1 na    ??                          <NA> 
##  8   620 OFF COMMUN AND NATL ACAD PRESS   OFF COMMUN AND NATL ACAD PRESS       1 na    ??                          <NA> 
##  9   625 OKLAHOMA MED RES FDN             OKLAHOMA MED RES FDN                 1 na    Oklahoma Medical Research … <NA> 
## 10   637 PRINCE EDWARD CTY HIGH SCH       PRINCE EDWARD CTY HIGH SCH           1 na    Prince Edward County High … Virg…
## # ℹ 219 more rows

## # A tibble: 7,904 × 4
## # Groups:   eligibility, Institution Name [1,043]
##    `Institution Name` name         value eligibility
##    <fct>              <chr>        <fct> <chr>      
##  1 Auburn University  AANAPISI     1     Ineligible 
##  2 Auburn University  AANAPISI F   1     Ineligible 
##  3 Auburn University  ANNH         1     Ineligible 
##  4 Auburn University  ANNH F       1     Ineligible 
##  5 Auburn University  HBCU         2     Ineligible 
##  6 Auburn University  HBCU Masters 2     Ineligible 
##  7 Auburn University  HBGI         2     Ineligible 
##  8 Auburn University  HSI          1     Ineligible 
##  9 Auburn University  HSI STEM     1     Ineligible 
## 10 Auburn University  NASNTI       1     Ineligible 
## # ℹ 7,894 more rows
##              [,1]             
## eligibility  "Current grantee"
## titled       "TRUE"           
## AANAPISI     NA               
## AANAPISI F   "10"             
## ANNH         NA               
## ANNH F       NA               
## HBCU         NA               
## HBCU Masters "1"              
## HBGI         NA               
## HSI          NA               
## HSI STEM     "23"             
## MSEIP        "9"              
## NASNTI       NA               
## NASNTI F     NA               
## PBI A        "2"              
## PBI F        "3"              
## PPOHA        "4"              
## SIP          "23"             
## TCCU         NA
## # A tibble: 9,112 × 4
## # Groups:   Institution Name, eligibility [1,056]
##    `Institution Name` name         value eligibility
##    <fct>              <chr>        <fct> <chr>      
##  1 Auburn University  AANAPISI     1     Ineligible 
##  2 Auburn University  AANAPISI F   1     Ineligible 
##  3 Auburn University  ANNH         1     Ineligible 
##  4 Auburn University  ANNH F       1     Ineligible 
##  5 Auburn University  HBCU         2     Ineligible 
##  6 Auburn University  HBCU Masters 2     Ineligible 
##  7 Auburn University  HBGI         2     Ineligible 
##  8 Auburn University  HSI          1     Ineligible 
##  9 Auburn University  HSI STEM     1     Ineligible 
## 10 Auburn University  NASNTI       1     Ineligible 
## # ℹ 9,102 more rows
## [1] 0.3967611
## [1] 0.1477733
## 
##   No  Yes 
## 1419 2016

What are the collaborative connections between institutions?

Most cited papers and journals

Multiple Correspondence Analysis

## Tab
##      STUDENT       BIOLOG   UNDERGRADU       SCIENC     RESEARCH        LEARN        TEACH         EDUC       EXPERI 
##          361          336          236          222          216          186          167          118           92 
## INTRODUCTORI      DEVELOP         STEM       ASSESS      PROGRAM        MODEL       IMPROV      PRACTIC      ANALYSI 
##           89           85           83           73           58           54           53           53           51 
##        COURS       COLLEG     SCIENTIF    CLASSROOM       EFFECT      CONCEPT      FACULTI 
##           51           50           50           49           49           48           47